Prediction of three-phase product distribution and bio-oil heating value of biomass fast pyrolysis based on machine learning

Abstract

In this work, by mining the experimental data of fast pyrolysis of lignocellulosic biomass in bubbling fluidized bed in previous literature, regression prediction models were established for three-phase product distribution and bio-oil heating value (HHV) based on gradient boosting, random forest, support vector machine, and multilayer perceptron algorithms. Comprehensive feedstock characteristics and pyrolysis conditions were considered and compared as input features. Among the several algorithms, random forest is most suitable for the prediction of three-phase product yields and bio-oil HHV with the benefits of high accuracy and good generalization ability. Visual analysis of the model shows that pyrolysis temperature is the most critical factor affecting three-phase product distribution, while bio-oil HHV is more affected by the feedstock characteristics such as the contents of C and H. The highest yield and HHV of bio-oil is obtained at about 480 °C, suggesting 480 °C as the optimum pyrolysis temperature of fast pyrolysis of biomass in a bubbling fluidized bed. As for the feedstock characteristics, high contents of C and H and low content of O are favorable to the enhancement of bio-oil HHV, indicating the crucial importance of feedstock pretreatment such as torrefaction to the quality improvement of bio-oil.